neurodynex.adex_model package

Submodules

neurodynex.adex_model.AdEx module

Implementation of the Adaptive Exponential Integrate-and-Fire model.

See Neuronal Dynamics Chapter 6 Section 1

neurodynex.adex_model.AdEx.getting_started()

Simple example to get started

neurodynex.adex_model.AdEx.plot_adex_state(adex_state_monitor)

Visualizes the state variables: w-t, v-t and phase-plane w-v

Parameters:adex_state_monitor (StateMonitor) – States of “v” and “w”

neurodynex.adex_model.AdEx.simulate_AdEx_neuron(tau_m=5. * msecond, R=0.5 * Gohm, v_rest=-70. * mvolt, v_reset=-51. * mvolt, v_rheobase=-50. * mvolt, a=0.5 * nsiemens, b=7. * pamp, v_spike=-30. * mvolt, delta_T=2. * mvolt, tau_w=100. * msecond, I_stim=<brian2.input.timedarray.TimedArray object>, simulation_time=200. * msecond)

Implementation of the AdEx model with a single adaptation variable w.

The Brian2 model equations are:

\[\begin{split}\frac{dv}{dt} = (-(v-v_rest) +delta_T*exp((v-v_rheobase)/delta_T)+ R * I_stim(t,i) - R * w)/(tau_m) : volt \\ \frac{dw}{dt} = (a*(v-v_rest)-w)/tau_w : amp\end{split}\]

Parameters:

• tau_m (Quantity) – membrane time scale
• R (Quantity) – membrane restistance
• v_rest (Quantity) – resting potential
• v_reset (Quantity) – reset potential
• v_rheobase (Quantity) – rheobase threshold
• a (Quantity) – Adaptation-Voltage coupling
• b (Quantity) – Spike-triggered adaptation current (=increment of w after each spike)
• v_spike (Quantity) – voltage threshold for the spike condition
• delta_T (Quantity) – Sharpness of the exponential term
• tau_w (Quantity) – Adaptation time constant
• I_stim (TimedArray) – Input current
• simulation_time (Quantity) – Duration for which the model is simulated

Returns:

A b2.StateMonitor for the variables “v” and “w” and a b2.SpikeMonitor

Return type:

(state_monitor, spike_monitor)

Module contents